解决问题
AttributeError: 'PathCollection' object has no property 'n_levels'
解决思路
属性错误:“PathCollection”对象没有属性“n_levels”
解决方法
def scatter Found at: matplotlib.pyplot中并没有n_levels参数！很可能是代码写的有误，这个参数存在在中，如果必须使用n_levels参数，那么应该加到sns.kdeplot函数中，即可！
def kdeplot Found at: seaborn.distributions
@_deprecate_positional_args
def kdeplot(
x= # Allow positional x, because behavior will not change with reorg
None,
*, y=None,
shade= # Note "soft" deprecation, explained below
None, vertical= # Deprecated
False, kernel= # Deprecated
None, bw= # Deprecated
None, gridsize= # TODO maybe depend on uni/bivariate?
200, cut=3, clip=None, legend=True, cumulative=False,
shade_lowest= # Deprecated, controlled with levels now
None, cbar=False, cbar_ax=None, cbar_kws=None,
ax=
# New params
None, weights= # TODO note that weights is grouped with
semantics
None, hue=None, palette=None, hue_order=None,
hue_norm=None,
multiple="layer", common_norm=True, common_grid=False,
levels=10, thresh=.05,
bw_method="scott", bw_adjust=1, log_scale=None,
color=None, fill=
# Renamed params
None, data=None, data2=None, **
kwargs):
# Handle deprecation of `data2` as name for y variable
if data2 is not None:
y = data2
# If `data2` is present, we need to check for the `data` kwarg being
# used to pass a vector for `x`. We'll reassign the vectors and
warn.
# We need this check because just passing a vector to `data` is
now
# technically valid.
x_passed_as_data = x is None and data is not None and np.ndim
(data) == 1
if x_passed_as_data:
msg = "Use `x` and `y` rather than `data` `and `data2`"
x = data
else:
msg = "The `data2` param is now named `y`; please update your
code"
warnings.warn(msg, FutureWarning)
# Handle deprecation of `vertical`
if vertical:
msg = "The `vertical` parameter is deprecated and will be
removed in a "\
"future version. Assign the data to the `y` variable instead."
warnings.warn(msg, FutureWarning)
x, y = y, x
# Handle deprecation of `bw`
if bw is not None:
msg = "The `bw` parameter is deprecated in favor of
`bw_method` and "\
f"`bw_adjust`. Using {bw} for `bw_method`, but please "\
"see the docs for the new parameters and update your code."
warnings.warn(msg, FutureWarning)
bw_method = bw
# Handle deprecation of `kernel`
if kernel is not None:
msg = "Support for alternate kernels has been removed. "\
"Using Gaussian kernel."
warnings.warn(msg, UserWarning)
# Handle deprecation of shade_lowest
if shade_lowest is not None:
if shade_lowest:
thresh = 0
msg = "`shade_lowest` is now deprecated in favor of `thresh`. "\
f"Setting `thresh={thresh}`, but please update your code."
warnings.warn(msg, UserWarning)
# Handle `n_levels`
# This was never in the formal API but it was processed, and
appeared in an
# example. We can treat as an alias for `levels` now and deprecate
later.
levels = kwargs.pop("n_levels", levels)
# Handle "soft" deprecation of shade `shade` is not really the right
# terminology here, but unlike some of the other deprecated
parameters it
# is probably very commonly used and much hard to remove. This
is therefore
# going to be a longer process where, first, `fill` will be introduced
and
# be used throughout the documentation. In 0.12, when kwarg-only
# enforcement hits, we can remove the shade/shade_lowest out of
the
# function signature all together and pull them out of the kwargs.
Then we
# can actually fire a FutureWarning, and eventually remove.
if shade is not None:
fill = shade
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
p = _DistributionPlotter(
data=data,
variables=_DistributionPlotter.get_semantics(locals()))
p.map_hue(palette=palette, order=hue_order, norm=hue_norm)
if ax is None:
ax = plt.gca()
# Check for a specification that lacks x/y data and return early
if not p.has_xy_data:
return ax
# Pack the kwargs for statistics.KDE
estimate_kws = dict(bw_method=bw_method,
bw_adjust=bw_adjust,
gridsize=gridsize,
cut=cut,
clip=clip,
cumulative=cumulative)
p._attach(ax, allowed_types=["numeric", "datetime"],
log_scale=log_scale)
if p.univariate:
plot_kws = kwargs.copy()
if color is not None:
plot_kws["color"] = color
p.plot_univariate_density(multiple=multiple,
common_norm=common_norm, common_grid=common_grid,
fill=fill, legend=legend, estimate_kws=estimate_kws, **plot_kws)
else:
p.plot_bivariate_density(common_norm=common_norm, fill=fill,
levels=levels, thresh=thresh, legend=legend, color=color, cbar=cbar,
cbar_ax=cbar_ax, cbar_kws=cbar_kws, estimate_kws=estimate_kws,
**kwargs)
return ax
